Overview

Dataset statistics

Number of variables33
Number of observations1053
Missing cells23
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory743.7 KiB
Average record size in memory723.2 B

Variable types

Text2
Categorical10
DateTime1
Numeric19
Boolean1

Dataset

DescriptionJHB_Ezin_002 - Quality-corrected harmonized data
CreatorRP2 Clinical Data Quality Team
AuthorQuality-Checked Data
URLHEAT Research Projects

Variable descriptions

Age (at enrolment)Patient age at study enrollment
CD4 cell count (cells/µL)CD4+ T lymphocyte count (missing codes removed)
HIV viral load (copies/mL)HIV RNA copies per mL (missing codes removed)
BMI (kg/m²)Body Mass Index (extreme values removed)
Waist circumference (cm)Waist circumference (corrected from mm to cm)
ALT (U/L)Alanine aminotransferase (missing codes removed)
Platelet count (×10³/µL)Platelet count (missing codes removed)
Hematocrit (%)Hematocrit (zero values removed)
Lymphocyte count (×10⁹/L)Lymphocyte absolute count (corrected labeling)
Neutrophil count (×10⁹/L)Neutrophil absolute count (corrected labeling)
cd4_correction_appliedQuality flag: CD4 missing codes removed
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circ unit corrected

Alerts

study_source has constant value "JHB_Ezin_002"Constant
latitude has constant value "-26.2041"Constant
longitude has constant value "28.0473"Constant
province has constant value "Gauteng"Constant
city has constant value "Johannesburg"Constant
jhb_subregion has constant value "Central_JHB"Constant
HIV_status has constant value "Positive"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
BMI (kg/m²) is highly overall correlated with weight_kgHigh correlation
CD4 cell count (cells/µL) is highly overall correlated with HIV viral load (copies/mL)High correlation
HIV viral load (copies/mL) is highly overall correlated with CD4 cell count (cells/µL)High correlation
Hematocrit (%) is highly overall correlated with Sex and 1 other fieldsHigh correlation
Lymphocyte count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Monocyte count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Neutrophil count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Sex is highly overall correlated with Hematocrit (%) and 2 other fieldsHigh correlation
White blood cell count (×10³/µL) is highly overall correlated with Lymphocyte count (×10⁹/L) and 2 other fieldsHigh correlation
diastolic_bp_mmHg is highly overall correlated with systolic_bp_mmHgHigh correlation
height_m is highly overall correlated with SexHigh correlation
hemoglobin_g_dL is highly overall correlated with Hematocrit (%) and 1 other fieldsHigh correlation
systolic_bp_mmHg is highly overall correlated with diastolic_bp_mmHgHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²)High correlation
anonymous_patient_id has unique valuesUnique
Patient ID has unique valuesUnique
Eosinophil count (×10⁹/L) has 47 (4.5%) zerosZeros
Basophil count (×10⁹/L) has 122 (11.6%) zerosZeros

Reproduction

Analysis started2025-11-24 21:49:24.653555
Analysis finished2025-11-24 21:49:37.958531
Duration13.3 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct1053
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
2025-11-24T23:49:37.997075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17901
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1053 ?
Unique (%)100.0%

Sample

1st rowHEAT_39B060AAE05B
2nd rowHEAT_777FE353096E
3rd rowHEAT_870B72D986FC
4th rowHEAT_322176AC6C5D
5th rowHEAT_3CEA7B846173
ValueCountFrequency (%)
heat_39b060aae05b1
 
0.1%
heat_65da0b7621901
 
0.1%
heat_870b72d986fc1
 
0.1%
heat_322176ac6c5d1
 
0.1%
heat_3cea7b8461731
 
0.1%
heat_58ff2f784cb21
 
0.1%
heat_8933f8561dfe1
 
0.1%
heat_b98ea6b0a97c1
 
0.1%
heat_4f1746b9846f1
 
0.1%
heat_28f38f9e703f1
 
0.1%
Other values (1043)1043
99.1%
2025-11-24T23:49:38.102514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E1855
 
10.4%
A1834
 
10.2%
H1053
 
5.9%
T1053
 
5.9%
_1053
 
5.9%
6829
 
4.6%
8821
 
4.6%
3815
 
4.6%
C808
 
4.5%
4808
 
4.5%
Other values (9)6972
38.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8965
50.1%
Decimal Number7883
44.0%
Connector Punctuation1053
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6829
10.5%
8821
10.4%
3815
10.3%
4808
10.2%
2792
10.0%
5787
10.0%
0774
9.8%
9762
9.7%
7748
9.5%
1747
9.5%
Uppercase Letter
ValueCountFrequency (%)
E1855
20.7%
A1834
20.5%
H1053
11.7%
T1053
11.7%
C808
9.0%
B807
9.0%
F784
8.7%
D771
8.6%
Connector Punctuation
ValueCountFrequency (%)
_1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8965
50.1%
Common8936
49.9%

Most frequent character per script

Common
ValueCountFrequency (%)
_1053
11.8%
6829
9.3%
8821
9.2%
3815
9.1%
4808
9.0%
2792
8.9%
5787
8.8%
0774
8.7%
9762
8.5%
7748
8.4%
Latin
ValueCountFrequency (%)
E1855
20.7%
A1834
20.5%
H1053
11.7%
T1053
11.7%
C808
9.0%
B807
9.0%
F784
8.7%
D771
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII17901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E1855
 
10.4%
A1834
 
10.2%
H1053
 
5.9%
T1053
 
5.9%
_1053
 
5.9%
6829
 
4.6%
8821
 
4.6%
3815
 
4.6%
C808
 
4.5%
4808
 
4.5%
Other values (9)6972
38.9%

Patient ID
Text

Unique 

Distinct1053
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2025-11-24T23:49:38.188454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7371
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1053 ?
Unique (%)100.0%

Sample

1st row01-0001
2nd row01-0002
3rd row01-0003
4th row01-0004
5th row01-0005
ValueCountFrequency (%)
01-00011
 
0.1%
01-00661
 
0.1%
01-00031
 
0.1%
01-00041
 
0.1%
01-00051
 
0.1%
01-00061
 
0.1%
01-00071
 
0.1%
01-00081
 
0.1%
01-00091
 
0.1%
01-00101
 
0.1%
Other values (1043)1043
99.1%
2025-11-24T23:49:38.315775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02434
33.0%
11404
19.0%
-1053
14.3%
3330
 
4.5%
2320
 
4.3%
7306
 
4.2%
6305
 
4.1%
9305
 
4.1%
5305
 
4.1%
4305
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6318
85.7%
Dash Punctuation1053
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02434
38.5%
11404
22.2%
3330
 
5.2%
2320
 
5.1%
7306
 
4.8%
6305
 
4.8%
9305
 
4.8%
5305
 
4.8%
4305
 
4.8%
8304
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7371
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02434
33.0%
11404
19.0%
-1053
14.3%
3330
 
4.5%
2320
 
4.3%
7306
 
4.2%
6305
 
4.1%
9305
 
4.1%
5305
 
4.1%
4305
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02434
33.0%
11404
19.0%
-1053
14.3%
3330
 
4.5%
2320
 
4.3%
7306
 
4.2%
6305
 
4.1%
9305
 
4.1%
5305
 
4.1%
4305
 
4.1%

study_source
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
JHB_Ezin_002
1053 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12636
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_Ezin_002
2nd rowJHB_Ezin_002
3rd rowJHB_Ezin_002
4th rowJHB_Ezin_002
5th rowJHB_Ezin_002

Common Values

ValueCountFrequency (%)
JHB_Ezin_0021053
100.0%

Length

2025-11-24T23:49:38.367540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.398625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_0021053
100.0%

Most occurring characters

ValueCountFrequency (%)
_2106
16.7%
02106
16.7%
J1053
8.3%
H1053
8.3%
B1053
8.3%
E1053
8.3%
z1053
8.3%
i1053
8.3%
n1053
8.3%
21053
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4212
33.3%
Decimal Number3159
25.0%
Lowercase Letter3159
25.0%
Connector Punctuation2106
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J1053
25.0%
H1053
25.0%
B1053
25.0%
E1053
25.0%
Lowercase Letter
ValueCountFrequency (%)
z1053
33.3%
i1053
33.3%
n1053
33.3%
Decimal Number
ValueCountFrequency (%)
02106
66.7%
21053
33.3%
Connector Punctuation
ValueCountFrequency (%)
_2106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7371
58.3%
Common5265
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J1053
14.3%
H1053
14.3%
B1053
14.3%
E1053
14.3%
z1053
14.3%
i1053
14.3%
n1053
14.3%
Common
ValueCountFrequency (%)
_2106
40.0%
02106
40.0%
21053
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_2106
16.7%
02106
16.7%
J1053
8.3%
H1053
8.3%
B1053
8.3%
E1053
8.3%
z1053
8.3%
i1053
8.3%
n1053
8.3%
21053
8.3%
Distinct268
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
Minimum2017-01-17 00:00:00
Maximum2018-05-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-24T23:49:38.434763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:38.479784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Age (at enrolment)
Real number (ℝ)

Patient age at study enrollment

Distinct45
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.460589
Minimum13
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:38.523117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile21
Q127
median32
Q337
95-th percentile46
Maximum62
Range49
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7363651
Coefficient of variation (CV)0.23833101
Kurtosis0.1552562
Mean32.460589
Median Absolute Deviation (MAD)5
Skewness0.52185785
Sum34181
Variance59.851345
MonotonicityNot monotonic
2025-11-24T23:49:38.566779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3166
 
6.3%
3356
 
5.3%
2955
 
5.2%
3053
 
5.0%
3250
 
4.7%
2649
 
4.7%
3548
 
4.6%
3448
 
4.6%
2447
 
4.5%
2847
 
4.5%
Other values (35)534
50.7%
ValueCountFrequency (%)
131
 
0.1%
141
 
0.1%
152
 
0.2%
171
 
0.1%
189
 
0.9%
198
 
0.8%
2017
1.6%
2119
1.8%
2226
2.5%
2331
2.9%
ValueCountFrequency (%)
621
 
0.1%
591
 
0.1%
572
 
0.2%
552
 
0.2%
544
0.4%
535
0.5%
523
0.3%
517
0.7%
504
0.4%
492
 
0.2%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Female
627 
Male
426 

Length

Max length6
Median length6
Mean length5.1908832
Min length4

Characters and Unicode

Total characters5466
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female627
59.5%
Male426
40.5%

Length

2025-11-24T23:49:38.611298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.647060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female627
59.5%
male426
40.5%

Most occurring characters

ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4413
80.7%
Uppercase Letter1053
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1680
38.1%
a1053
23.9%
l1053
23.9%
m627
 
14.2%
Uppercase Letter
ValueCountFrequency (%)
F627
59.5%
M426
40.5%

Most occurring scripts

ValueCountFrequency (%)
Latin5466
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

latitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.8 KiB
-26.2041
1053 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8424
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-26.2041
2nd row-26.2041
3rd row-26.2041
4th row-26.2041
5th row-26.2041

Common Values

ValueCountFrequency (%)
-26.20411053
100.0%

Length

2025-11-24T23:49:38.685727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.718804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.20411053
100.0%

Most occurring characters

ValueCountFrequency (%)
22106
25.0%
-1053
12.5%
61053
12.5%
.1053
12.5%
01053
12.5%
41053
12.5%
11053
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6318
75.0%
Dash Punctuation1053
 
12.5%
Other Punctuation1053
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22106
33.3%
61053
16.7%
01053
16.7%
41053
16.7%
11053
16.7%
Dash Punctuation
ValueCountFrequency (%)
-1053
100.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22106
25.0%
-1053
12.5%
61053
12.5%
.1053
12.5%
01053
12.5%
41053
12.5%
11053
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22106
25.0%
-1053
12.5%
61053
12.5%
.1053
12.5%
01053
12.5%
41053
12.5%
11053
12.5%

longitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
28.0473
1053 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7371
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.04731053
100.0%

Length

2025-11-24T23:49:38.753568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.786765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.04731053
100.0%

Most occurring characters

ValueCountFrequency (%)
21053
14.3%
81053
14.3%
.1053
14.3%
01053
14.3%
41053
14.3%
71053
14.3%
31053
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6318
85.7%
Other Punctuation1053
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21053
16.7%
81053
16.7%
01053
16.7%
41053
16.7%
71053
16.7%
31053
16.7%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7371
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21053
14.3%
81053
14.3%
.1053
14.3%
01053
14.3%
41053
14.3%
71053
14.3%
31053
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21053
14.3%
81053
14.3%
.1053
14.3%
01053
14.3%
41053
14.3%
71053
14.3%
31053
14.3%

province
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
Gauteng
1053 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7371
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng1053
100.0%

Length

2025-11-24T23:49:38.820039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.851993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng1053
100.0%

Most occurring characters

ValueCountFrequency (%)
G1053
14.3%
a1053
14.3%
u1053
14.3%
t1053
14.3%
e1053
14.3%
n1053
14.3%
g1053
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6318
85.7%
Uppercase Letter1053
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1053
16.7%
u1053
16.7%
t1053
16.7%
e1053
16.7%
n1053
16.7%
g1053
16.7%
Uppercase Letter
ValueCountFrequency (%)
G1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7371
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1053
14.3%
a1053
14.3%
u1053
14.3%
t1053
14.3%
e1053
14.3%
n1053
14.3%
g1053
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1053
14.3%
a1053
14.3%
u1053
14.3%
t1053
14.3%
e1053
14.3%
n1053
14.3%
g1053
14.3%

city
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
Johannesburg
1053 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12636
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg1053
100.0%

Length

2025-11-24T23:49:38.883899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.914699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg1053
100.0%

Most occurring characters

ValueCountFrequency (%)
n2106
16.7%
J1053
8.3%
o1053
8.3%
h1053
8.3%
a1053
8.3%
e1053
8.3%
s1053
8.3%
b1053
8.3%
u1053
8.3%
r1053
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11583
91.7%
Uppercase Letter1053
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2106
18.2%
o1053
9.1%
h1053
9.1%
a1053
9.1%
e1053
9.1%
s1053
9.1%
b1053
9.1%
u1053
9.1%
r1053
9.1%
g1053
9.1%
Uppercase Letter
ValueCountFrequency (%)
J1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12636
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2106
16.7%
J1053
8.3%
o1053
8.3%
h1053
8.3%
a1053
8.3%
e1053
8.3%
s1053
8.3%
b1053
8.3%
u1053
8.3%
r1053
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2106
16.7%
J1053
8.3%
o1053
8.3%
h1053
8.3%
a1053
8.3%
e1053
8.3%
s1053
8.3%
b1053
8.3%
u1053
8.3%
r1053
8.3%

jhb_subregion
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
Central_JHB
1053 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11583
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB1053
100.0%

Length

2025-11-24T23:49:38.947283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:38.977941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb1053
100.0%

Most occurring characters

ValueCountFrequency (%)
C1053
9.1%
e1053
9.1%
n1053
9.1%
t1053
9.1%
r1053
9.1%
a1053
9.1%
l1053
9.1%
_1053
9.1%
J1053
9.1%
H1053
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6318
54.5%
Uppercase Letter4212
36.4%
Connector Punctuation1053
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1053
16.7%
n1053
16.7%
t1053
16.7%
r1053
16.7%
a1053
16.7%
l1053
16.7%
Uppercase Letter
ValueCountFrequency (%)
C1053
25.0%
J1053
25.0%
H1053
25.0%
B1053
25.0%
Connector Punctuation
ValueCountFrequency (%)
_1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10530
90.9%
Common1053
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C1053
10.0%
e1053
10.0%
n1053
10.0%
t1053
10.0%
r1053
10.0%
a1053
10.0%
l1053
10.0%
J1053
10.0%
H1053
10.0%
B1053
10.0%
Common
ValueCountFrequency (%)
_1053
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C1053
9.1%
e1053
9.1%
n1053
9.1%
t1053
9.1%
r1053
9.1%
a1053
9.1%
l1053
9.1%
_1053
9.1%
J1053
9.1%
H1053
9.1%

CD4 cell count (cells/µL)
Real number (ℝ)

High correlation 

CD4+ T lymphocyte count (missing codes removed)

Distinct903
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.014084
Minimum0.26
Maximum54.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.014851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile3.792
Q111.28
median17.08
Q323.62
95-th percentile34.336
Maximum54.13
Range53.87
Interquartile range (IQR)12.34

Descriptive statistics

Standard deviation9.1601515
Coefficient of variation (CV)0.50849944
Kurtosis-0.0048244797
Mean18.014084
Median Absolute Deviation (MAD)6.21
Skewness0.44891061
Sum18968.83
Variance83.908375
MonotonicityNot monotonic
2025-11-24T23:49:39.064062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.544
 
0.4%
22.684
 
0.4%
17.673
 
0.3%
16.53
 
0.3%
14.993
 
0.3%
5.583
 
0.3%
12.843
 
0.3%
16.933
 
0.3%
10.493
 
0.3%
17.743
 
0.3%
Other values (893)1021
97.0%
ValueCountFrequency (%)
0.261
0.1%
0.341
0.1%
0.451
0.1%
0.531
0.1%
0.631
0.1%
0.651
0.1%
0.71
0.1%
0.771
0.1%
0.831
0.1%
0.911
0.1%
ValueCountFrequency (%)
54.131
0.1%
50.631
0.1%
481
0.1%
44.711
0.1%
44.651
0.1%
44.611
0.1%
43.651
0.1%
43.071
0.1%
42.381
0.1%
41.451
0.1%

HIV viral load (copies/mL)
Real number (ℝ)

High correlation 

HIV RNA copies per mL (missing codes removed)

Distinct977
Distinct (%)93.0%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean88703.72
Minimum0
Maximum4117370
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.111265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile971.5
Q15839.5
median24796
Q379879
95-th percentile361649.5
Maximum4117370
Range4117370
Interquartile range (IQR)74039.5

Descriptive statistics

Standard deviation231648.72
Coefficient of variation (CV)2.6114882
Kurtosis117.98199
Mean88703.72
Median Absolute Deviation (MAD)21970
Skewness8.9610023
Sum93227610
Variance5.3661128 × 1010
MonotonicityNot monotonic
2025-11-24T23:49:39.156538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
839803
 
0.3%
39553
 
0.3%
120493
 
0.3%
6412
 
0.2%
8892
 
0.2%
3763632
 
0.2%
375252
 
0.2%
36922
 
0.2%
1071472
 
0.2%
7652
 
0.2%
Other values (967)1028
97.6%
ValueCountFrequency (%)
01
0.1%
5011
0.1%
5051
0.1%
5121
0.1%
5271
0.1%
5281
0.1%
5351
0.1%
5411
0.1%
5531
0.1%
5561
0.1%
ValueCountFrequency (%)
41173701
0.1%
27572981
0.1%
23246901
0.1%
17604591
0.1%
15519881
0.1%
13932131
0.1%
12039331
0.1%
11350111
0.1%
11103452
0.2%
10005911
0.1%

HIV_status
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.8 KiB
Positive
1053 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8424
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive1053
100.0%

Length

2025-11-24T23:49:39.201400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:39.234962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive1053
100.0%

Most occurring characters

ValueCountFrequency (%)
i2106
25.0%
P1053
12.5%
o1053
12.5%
s1053
12.5%
t1053
12.5%
v1053
12.5%
e1053
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7371
87.5%
Uppercase Letter1053
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2106
28.6%
o1053
14.3%
s1053
14.3%
t1053
14.3%
v1053
14.3%
e1053
14.3%
Uppercase Letter
ValueCountFrequency (%)
P1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8424
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2106
25.0%
P1053
12.5%
o1053
12.5%
s1053
12.5%
t1053
12.5%
v1053
12.5%
e1053
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2106
25.0%
P1053
12.5%
o1053
12.5%
s1053
12.5%
t1053
12.5%
v1053
12.5%
e1053
12.5%

Hematocrit (%)
Real number (ℝ)

High correlation 

Hematocrit (zero values removed)

Distinct39
Distinct (%)3.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.671958
Minimum21
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.267454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q137
median41
Q344
95-th percentile49
Maximum54
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.326574
Coefficient of variation (CV)0.13096429
Kurtosis0.29229504
Mean40.671958
Median Absolute Deviation (MAD)3
Skewness-0.47071382
Sum42786.9
Variance28.372391
MonotonicityNot monotonic
2025-11-24T23:49:39.313222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4289
 
8.5%
4188
 
8.4%
4381
 
7.7%
4080
 
7.6%
4571
 
6.7%
3967
 
6.4%
3765
 
6.2%
4464
 
6.1%
3856
 
5.3%
3650
 
4.7%
Other values (29)341
32.4%
ValueCountFrequency (%)
211
 
0.1%
232
 
0.2%
254
 
0.4%
266
 
0.6%
273
 
0.3%
287
0.7%
2914
1.3%
3013
1.2%
3117
1.6%
3214
1.3%
ValueCountFrequency (%)
544
 
0.4%
531
 
0.1%
521
 
0.1%
516
 
0.6%
5027
2.6%
4920
1.9%
4837
3.5%
47.81
 
0.1%
4742
4.0%
4642
4.0%

White blood cell count (×10³/µL)
Real number (ℝ)

High correlation 

Distinct496
Distinct (%)47.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.261635
Minimum1.52
Maximum24.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.359296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile2.9455
Q14.0475
median4.95
Q36.1
95-th percentile8.6335
Maximum24.68
Range23.16
Interquartile range (IQR)2.0525

Descriptive statistics

Standard deviation1.9325762
Coefficient of variation (CV)0.36729576
Kurtosis13.505495
Mean5.261635
Median Absolute Deviation (MAD)1.03
Skewness2.3037535
Sum5535.24
Variance3.7348508
MonotonicityNot monotonic
2025-11-24T23:49:39.486871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.478
 
0.8%
4.058
 
0.8%
5.77
 
0.7%
4.957
 
0.7%
4.477
 
0.7%
4.757
 
0.7%
5.596
 
0.6%
5.16
 
0.6%
4.086
 
0.6%
4.076
 
0.6%
Other values (486)984
93.4%
ValueCountFrequency (%)
1.521
0.1%
1.711
0.1%
1.821
0.1%
1.851
0.1%
1.941
0.1%
2.141
0.1%
2.171
0.1%
2.191
0.1%
2.221
0.1%
2.231
0.1%
ValueCountFrequency (%)
24.681
0.1%
17.961
0.1%
15.781
0.1%
15.331
0.1%
14.81
0.1%
13.211
0.1%
13.031
0.1%
12.421
0.1%
12.071
0.1%
11.931
0.1%

Platelet count (×10³/µL)
Real number (ℝ)

Platelet count (missing codes removed)

Distinct306
Distinct (%)29.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean265.84791
Minimum7
Maximum884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.532855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile160.55
Q1214
median258
Q3303
95-th percentile409
Maximum884
Range877
Interquartile range (IQR)89

Descriptive statistics

Standard deviation82.110469
Coefficient of variation (CV)0.30886257
Kurtosis5.8713216
Mean265.84791
Median Absolute Deviation (MAD)44
Skewness1.3912982
Sum279672
Variance6742.1291
MonotonicityNot monotonic
2025-11-24T23:49:39.577663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26113
 
1.2%
21611
 
1.0%
24010
 
0.9%
28710
 
0.9%
2129
 
0.9%
2669
 
0.9%
2229
 
0.9%
2449
 
0.9%
2199
 
0.9%
2819
 
0.9%
Other values (296)954
90.6%
ValueCountFrequency (%)
71
0.1%
171
0.1%
421
0.1%
631
0.1%
801
0.1%
821
0.1%
871
0.1%
901
0.1%
961
0.1%
982
0.2%
ValueCountFrequency (%)
8841
0.1%
7511
0.1%
6761
0.1%
6521
0.1%
6191
0.1%
5751
0.1%
5521
0.1%
5491
0.1%
5441
0.1%
5421
0.1%

hemoglobin_g_dL
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)9.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.298669
Minimum6.1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.621947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile9.9
Q112.3
median13.4
Q314.6
95-th percentile16.1
Maximum18
Range11.9
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.8591093
Coefficient of variation (CV)0.13979664
Kurtosis0.50078028
Mean13.298669
Median Absolute Deviation (MAD)1.2
Skewness-0.54074656
Sum13990.2
Variance3.4562875
MonotonicityNot monotonic
2025-11-24T23:49:39.672251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.432
 
3.0%
12.530
 
2.8%
13.928
 
2.7%
13.827
 
2.6%
13.325
 
2.4%
13.524
 
2.3%
13.224
 
2.3%
13.624
 
2.3%
14.424
 
2.3%
14.723
 
2.2%
Other values (93)791
75.1%
ValueCountFrequency (%)
6.11
 
0.1%
6.81
 
0.1%
7.21
 
0.1%
7.31
 
0.1%
7.41
 
0.1%
7.61
 
0.1%
7.73
0.3%
7.91
 
0.1%
83
0.3%
8.21
 
0.1%
ValueCountFrequency (%)
181
0.1%
17.81
0.1%
17.61
0.1%
17.51
0.1%
17.41
0.1%
17.32
0.2%
17.21
0.1%
17.11
0.1%
172
0.2%
16.92
0.2%

Lymphocyte count (×10⁹/L)
Real number (ℝ)

High correlation 

Lymphocyte absolute count (corrected labeling)

Distinct295
Distinct (%)28.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.8269962
Minimum0.21
Maximum9.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.721027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.81
Q11.3
median1.73
Q32.24
95-th percentile3.1645
Maximum9.94
Range9.73
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.77925427
Coefficient of variation (CV)0.42652211
Kurtosis12.036719
Mean1.8269962
Median Absolute Deviation (MAD)0.47
Skewness1.8291566
Sum1922
Variance0.60723721
MonotonicityNot monotonic
2025-11-24T23:49:39.769533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8211
 
1.0%
1.5911
 
1.0%
1.5811
 
1.0%
1.811
 
1.0%
1.5311
 
1.0%
1.3311
 
1.0%
1.9311
 
1.0%
1.6610
 
0.9%
1.6110
 
0.9%
1.4410
 
0.9%
Other values (285)945
89.7%
ValueCountFrequency (%)
0.211
 
0.1%
0.221
 
0.1%
0.251
 
0.1%
0.271
 
0.1%
0.331
 
0.1%
0.372
0.2%
0.451
 
0.1%
0.463
0.3%
0.471
 
0.1%
0.491
 
0.1%
ValueCountFrequency (%)
9.941
0.1%
5.921
0.1%
5.321
0.1%
4.731
0.1%
4.681
0.1%
4.211
0.1%
4.21
0.1%
4.161
0.1%
4.141
0.1%
4.111
0.1%

Neutrophil count (×10⁹/L)
Real number (ℝ)

High correlation 

Neutrophil absolute count (corrected labeling)

Distinct393
Distinct (%)37.5%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.7616619
Minimum0.19
Maximum9.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.814588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile1.25
Q11.88
median2.52
Q33.35
95-th percentile5.311
Maximum9.31
Range9.12
Interquartile range (IQR)1.47

Descriptive statistics

Standard deviation1.2519029
Coefficient of variation (CV)0.45331506
Kurtosis2.9788724
Mean2.7616619
Median Absolute Deviation (MAD)0.68
Skewness1.4142769
Sum2891.46
Variance1.5672609
MonotonicityNot monotonic
2025-11-24T23:49:39.860136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.668
 
0.8%
1.858
 
0.8%
3.728
 
0.8%
2.648
 
0.8%
2.368
 
0.8%
2.717
 
0.7%
2.497
 
0.7%
2.27
 
0.7%
2.157
 
0.7%
1.937
 
0.7%
Other values (383)972
92.3%
ValueCountFrequency (%)
0.191
0.1%
0.652
0.2%
0.721
0.1%
0.811
0.1%
0.862
0.2%
0.91
0.1%
0.922
0.2%
0.962
0.2%
0.992
0.2%
11
0.1%
ValueCountFrequency (%)
9.311
0.1%
9.171
0.1%
8.521
0.1%
8.071
0.1%
7.961
0.1%
7.661
0.1%
7.431
0.1%
7.41
0.1%
71
0.1%
6.991
0.1%

Monocyte count (×10⁹/L)
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)9.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.4538308
Minimum0.11
Maximum2.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.903662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.22
Q10.33
median0.42
Q30.54
95-th percentile0.79
Maximum2.12
Range2.01
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.19087226
Coefficient of variation (CV)0.42058022
Kurtosis10.270507
Mean0.4538308
Median Absolute Deviation (MAD)0.1
Skewness2.0631299
Sum477.43
Variance0.036432219
MonotonicityNot monotonic
2025-11-24T23:49:39.952356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4333
 
3.1%
0.3433
 
3.1%
0.4132
 
3.0%
0.431
 
2.9%
0.3830
 
2.8%
0.3630
 
2.8%
0.3529
 
2.8%
0.4829
 
2.8%
0.2928
 
2.7%
0.4628
 
2.7%
Other values (86)749
71.1%
ValueCountFrequency (%)
0.114
 
0.4%
0.122
 
0.2%
0.143
 
0.3%
0.154
 
0.4%
0.163
 
0.3%
0.171
 
0.1%
0.186
0.6%
0.194
 
0.4%
0.26
0.6%
0.2112
1.1%
ValueCountFrequency (%)
2.121
0.1%
1.841
0.1%
1.541
0.1%
1.471
0.1%
1.421
0.1%
1.181
0.1%
1.171
0.1%
1.081
0.1%
1.072
0.2%
1.041
0.1%

Eosinophil count (×10⁹/L)
Real number (ℝ)

Zeros 

Distinct88
Distinct (%)8.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.14214829
Minimum0
Maximum3.16
Zeros47
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:39.999427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.03
median0.075
Q30.16
95-th percentile0.51
Maximum3.16
Range3.16
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.22564749
Coefficient of variation (CV)1.5874091
Kurtosis44.797394
Mean0.14214829
Median Absolute Deviation (MAD)0.055
Skewness5.3004426
Sum149.54
Variance0.050916789
MonotonicityNot monotonic
2025-11-24T23:49:40.045910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0187
 
8.3%
0.0285
 
8.1%
0.0370
 
6.6%
0.0762
 
5.9%
0.0459
 
5.6%
0.0659
 
5.6%
0.0557
 
5.4%
047
 
4.5%
0.0844
 
4.2%
0.1240
 
3.8%
Other values (78)442
42.0%
ValueCountFrequency (%)
047
4.5%
0.0187
8.3%
0.0285
8.1%
0.0370
6.6%
0.0459
5.6%
0.0557
5.4%
0.0659
5.6%
0.0762
5.9%
0.0844
4.2%
0.0937
3.5%
ValueCountFrequency (%)
3.161
0.1%
1.891
0.1%
1.752
0.2%
1.571
0.1%
1.511
0.1%
1.481
0.1%
1.351
0.1%
1.251
0.1%
1.232
0.2%
1.151
0.1%

Basophil count (×10⁹/L)
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)1.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.024030418
Minimum0
Maximum0.21
Zeros122
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.086080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.03
95-th percentile0.05
Maximum0.21
Range0.21
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.019359659
Coefficient of variation (CV)0.80563139
Kurtosis13.357364
Mean0.024030418
Median Absolute Deviation (MAD)0.01
Skewness2.4211104
Sum25.28
Variance0.00037479641
MonotonicityNot monotonic
2025-11-24T23:49:40.124148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.02293
27.8%
0.03217
20.6%
0.01217
20.6%
0122
11.6%
0.04100
 
9.5%
0.0551
 
4.8%
0.0619
 
1.8%
0.0712
 
1.1%
0.096
 
0.6%
0.085
 
0.5%
Other values (5)10
 
0.9%
ValueCountFrequency (%)
0122
11.6%
0.01217
20.6%
0.02293
27.8%
0.03217
20.6%
0.04100
 
9.5%
0.0551
 
4.8%
0.0619
 
1.8%
0.0712
 
1.1%
0.085
 
0.5%
0.096
 
0.6%
ValueCountFrequency (%)
0.211
 
0.1%
0.161
 
0.1%
0.132
 
0.2%
0.114
 
0.4%
0.12
 
0.2%
0.096
 
0.6%
0.085
 
0.5%
0.0712
 
1.1%
0.0619
 
1.8%
0.0551
4.8%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Body Mass Index (extreme values removed)

Distinct1016
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.11742
Minimum15.269471
Maximum49.672814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.164947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.269471
5-th percentile17.880777
Q120.15625
median23.084852
Q326.794938
95-th percentile33.960395
Maximum49.672814
Range34.403343
Interquartile range (IQR)6.638688

Descriptive statistics

Standard deviation5.3163571
Coefficient of variation (CV)0.2204364
Kurtosis2.3289041
Mean24.11742
Median Absolute Deviation (MAD)3.2616416
Skewness1.2722863
Sum25395.643
Variance28.263653
MonotonicityNot monotonic
2025-11-24T23:49:40.212246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.805922492
 
0.2%
21.287770042
 
0.2%
22.817460322
 
0.2%
20.661157022
 
0.2%
19.887258852
 
0.2%
20.861119662
 
0.2%
19.395918372
 
0.2%
24.241544492
 
0.2%
21.241004922
 
0.2%
24.394463672
 
0.2%
Other values (1006)1033
98.1%
ValueCountFrequency (%)
15.269471081
0.1%
15.338972351
0.1%
15.377500291
0.1%
15.445162251
0.1%
15.480864371
0.1%
15.495867771
0.1%
15.50173011
0.1%
15.515143321
0.1%
15.561339971
0.1%
15.637645971
0.1%
ValueCountFrequency (%)
49.67281381
0.1%
47.498964961
0.1%
47.199265381
0.1%
47.075962541
0.1%
45.800944981
0.1%
45.28925621
0.1%
44.90195341
0.1%
44.708956311
0.1%
43.490608141
0.1%
40.731875531
0.1%

weight_kg
Real number (ℝ)

High correlation 

Distinct449
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.773694
Minimum41.3
Maximum133.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.260660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41.3
5-th percentile49.4
Q158.7
median66.4
Q377
95-th percentile96.42
Maximum133.6
Range92.3
Interquartile range (IQR)18.3

Descriptive statistics

Standard deviation14.262708
Coefficient of variation (CV)0.20738609
Kurtosis1.5419145
Mean68.773694
Median Absolute Deviation (MAD)8.8
Skewness1.0350275
Sum72418.7
Variance203.42483
MonotonicityNot monotonic
2025-11-24T23:49:40.307538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.410
 
0.9%
59.410
 
0.9%
71.18
 
0.8%
68.18
 
0.8%
66.87
 
0.7%
66.17
 
0.7%
64.47
 
0.7%
57.46
 
0.6%
61.56
 
0.6%
48.56
 
0.6%
Other values (439)978
92.9%
ValueCountFrequency (%)
41.31
0.1%
42.81
0.1%
431
0.1%
43.21
0.1%
43.31
0.1%
43.81
0.1%
441
0.1%
44.41
0.1%
44.81
0.1%
45.41
0.1%
ValueCountFrequency (%)
133.61
0.1%
128.51
0.1%
126.21
0.1%
123.31
0.1%
123.21
0.1%
1211
0.1%
120.21
0.1%
119.31
0.1%
117.41
0.1%
117.31
0.1%

height_m
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6932479
Minimum1.32
Maximum2.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.353471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile1.54
Q11.62
median1.69
Q31.77
95-th percentile1.86
Maximum2.01
Range0.69
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1001059
Coefficient of variation (CV)0.059120639
Kurtosis-0.019764094
Mean1.6932479
Median Absolute Deviation (MAD)0.07
Skewness0.14414148
Sum1782.99
Variance0.01002119
MonotonicityNot monotonic
2025-11-24T23:49:40.399720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6448
 
4.6%
1.7746
 
4.4%
1.6742
 
4.0%
1.6542
 
4.0%
1.741
 
3.9%
1.6641
 
3.9%
1.7139
 
3.7%
1.6239
 
3.7%
1.6338
 
3.6%
1.6936
 
3.4%
Other values (47)641
60.9%
ValueCountFrequency (%)
1.321
 
0.1%
1.351
 
0.1%
1.361
 
0.1%
1.442
 
0.2%
1.452
 
0.2%
1.461
 
0.1%
1.482
 
0.2%
1.56
0.6%
1.516
0.6%
1.529
0.9%
ValueCountFrequency (%)
2.012
 
0.2%
1.991
 
0.1%
1.971
 
0.1%
1.962
 
0.2%
1.951
 
0.1%
1.941
 
0.1%
1.935
0.5%
1.923
0.3%
1.913
0.3%
1.94
0.4%

heart_rate_bpm
Real number (ℝ)

Distinct71
Distinct (%)6.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean78.706274
Minimum50
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.446539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile58
Q169
median78
Q388
95-th percentile102
Maximum135
Range85
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.512169
Coefficient of variation (CV)0.17167843
Kurtosis0.11364238
Mean78.706274
Median Absolute Deviation (MAD)10
Skewness0.36895181
Sum82799
Variance182.57872
MonotonicityNot monotonic
2025-11-24T23:49:40.492801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7439
 
3.7%
8135
 
3.3%
7834
 
3.2%
8232
 
3.0%
8832
 
3.0%
7930
 
2.8%
7630
 
2.8%
6930
 
2.8%
8929
 
2.8%
8029
 
2.8%
Other values (61)732
69.5%
ValueCountFrequency (%)
503
 
0.3%
512
 
0.2%
526
0.6%
536
0.6%
5410
0.9%
556
0.6%
5610
0.9%
576
0.6%
5813
1.2%
5914
1.3%
ValueCountFrequency (%)
1351
0.1%
1301
0.1%
1241
0.1%
1221
0.1%
1191
0.1%
1171
0.1%
1152
0.2%
1141
0.1%
1121
0.1%
1112
0.2%

systolic_bp_mmHg
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.9867
Minimum87
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.536815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile100
Q1112
median122
Q3132
95-th percentile148
Maximum204
Range117
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.366555
Coefficient of variation (CV)0.12494484
Kurtosis2.2558664
Mean122.9867
Median Absolute Deviation (MAD)10
Skewness0.8395301
Sum129505
Variance236.131
MonotonicityNot monotonic
2025-11-24T23:49:40.584238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13036
 
3.4%
11933
 
3.1%
12132
 
3.0%
12431
 
2.9%
11631
 
2.9%
11131
 
2.9%
13630
 
2.8%
11030
 
2.8%
13230
 
2.8%
13728
 
2.7%
Other values (79)741
70.4%
ValueCountFrequency (%)
871
 
0.1%
891
 
0.1%
901
 
0.1%
913
 
0.3%
922
 
0.2%
931
 
0.1%
944
0.4%
955
0.5%
969
0.9%
975
0.5%
ValueCountFrequency (%)
2041
0.1%
1972
0.2%
1901
0.1%
1851
0.1%
1761
0.1%
1741
0.1%
1731
0.1%
1722
0.2%
1702
0.2%
1692
0.2%

diastolic_bp_mmHg
Real number (ℝ)

High correlation 

Distinct74
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.189934
Minimum39
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.708527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile61
Q171
median79
Q385
95-th percentile100
Maximum132
Range93
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.915419
Coefficient of variation (CV)0.15046633
Kurtosis1.5170025
Mean79.189934
Median Absolute Deviation (MAD)7
Skewness0.64779312
Sum83387
Variance141.9772
MonotonicityNot monotonic
2025-11-24T23:49:40.753552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8252
 
4.9%
7951
 
4.8%
8544
 
4.2%
8344
 
4.2%
8043
 
4.1%
8842
 
4.0%
7238
 
3.6%
7735
 
3.3%
8435
 
3.3%
7335
 
3.3%
Other values (64)634
60.2%
ValueCountFrequency (%)
391
 
0.1%
502
 
0.2%
512
 
0.2%
523
0.3%
544
0.4%
554
0.4%
561
 
0.1%
573
0.3%
585
0.5%
594
0.4%
ValueCountFrequency (%)
1321
 
0.1%
1272
0.2%
1231
 
0.1%
1211
 
0.1%
1201
 
0.1%
1181
 
0.1%
1173
0.3%
1161
 
0.1%
1151
 
0.1%
1142
0.2%

body_temperature_celsius
Real number (ℝ)

Distinct27
Distinct (%)2.6%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean36.519962
Minimum36
Maximum39.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-24T23:49:40.794303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile36
Q136.15
median36.4
Q336.8
95-th percentile37.3
Maximum39.9
Range3.9
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.45661989
Coefficient of variation (CV)0.012503296
Kurtosis6.5203939
Mean36.519962
Median Absolute Deviation (MAD)0.3
Skewness1.6311046
Sum38236.4
Variance0.20850172
MonotonicityNot monotonic
2025-11-24T23:49:40.839023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36150
14.2%
36.1112
10.6%
36.2100
9.5%
36.798
9.3%
36.487
8.3%
36.379
7.5%
36.573
6.9%
36.867
6.4%
36.664
6.1%
3760
 
5.7%
Other values (17)157
14.9%
ValueCountFrequency (%)
36150
14.2%
36.1112
10.6%
36.2100
9.5%
36.379
7.5%
36.487
8.3%
36.573
6.9%
36.664
6.1%
36.798
9.3%
36.867
6.4%
36.947
 
4.5%
ValueCountFrequency (%)
39.91
 
0.1%
39.71
 
0.1%
39.11
 
0.1%
38.91
 
0.1%
38.51
 
0.1%
38.42
 
0.2%
38.11
 
0.1%
37.91
 
0.1%
37.81
 
0.1%
37.77
0.7%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 missing codes removed

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
0.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01053
100.0%

Length

2025-11-24T23:49:40.889714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:40.921605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02106
100.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
1.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01053
100.0%

Length

2025-11-24T23:49:40.955443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-24T23:49:40.986603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11053
50.0%
01053
50.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circ unit corrected

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
False
1053 
ValueCountFrequency (%)
False1053
100.0%
2025-11-24T23:49:41.012157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2025-11-24T23:49:36.849457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:24.904748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.683238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.310831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.935687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.581646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.289967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.941264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.584493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.214899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.920665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.587838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.245920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.963864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.616504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.227488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.901352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.587064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.225302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.884654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:24.942159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.714565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.341740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.967547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.611484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.321381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.976751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.616224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.332097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.954547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.620427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.278186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.996442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.646858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.260757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.931602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.618554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.255531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.919366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.015625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.746135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.375285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.999867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.643697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.353152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.010074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.649200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.364125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.989424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.653097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.310715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.030733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.677789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.295262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.963464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.651799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.289358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.954229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.055305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.779739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.404824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.035018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.675149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.385334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.044243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.682482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.395359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.022322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.686736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.344230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.063620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.709774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.330121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.077884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.685705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.321564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.991719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.087340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.814266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.439503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.070182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.708824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.418826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.081127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.716861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.429669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.060567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.722256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.381038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.099573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.742618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.366041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.111362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.721017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.357521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:37.027274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.135638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.846164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.472620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.103811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.750429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.450009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.113213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.749495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.464187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.094638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.756890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.414751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.133025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.773991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.398805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.142150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.754376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.390038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:37.062299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.176148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.876587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.502555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.136481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.782491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.479975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.146527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.780423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.494263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.127607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.791709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.445743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.168001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.805641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.444693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.172341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.785511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.421272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:37.100299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.209000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.910929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.536756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.172249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.816587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:28.512848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.180268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:29.814555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:30.527928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.163515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:31.826841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:32.480536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.201991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:33.838181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:34.480207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.205455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.821637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.455484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:37.135360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.243966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:25.944054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:26.568757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.205610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:27.848061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-24T23:49:34.861530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:35.550642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.185029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-24T23:49:36.813544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-24T23:49:41.041291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Basophil count (×10⁹/L)CD4 cell count (cells/µL)Eosinophil count (×10⁹/L)HIV viral load (copies/mL)Hematocrit (%)Lymphocyte count (×10⁹/L)Monocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Platelet count (×10³/µL)SexWhite blood cell count (×10³/µL)body_temperature_celsiusdiastolic_bp_mmHgheart_rate_bpmheight_mhemoglobin_g_dLsystolic_bp_mmHgweight_kg
Age (at enrolment)1.0000.1630.009-0.1610.0040.054-0.080-0.111-0.035-0.106-0.0550.142-0.1310.0130.3140.0340.103-0.0720.2770.231
BMI (kg/m²)0.1631.0000.0120.0870.004-0.141-0.1210.1260.0630.0550.0950.3630.0880.0450.2000.120-0.360-0.1110.1320.800
Basophil count (×10⁹/L)0.0090.0121.0000.1180.174-0.1440.0750.3900.2360.1830.1510.0000.3490.002-0.005-0.030-0.0150.0660.0100.003
CD4 cell count (cells/µL)-0.1610.0870.1181.000-0.019-0.5070.1740.182-0.0540.1640.0650.0720.179-0.100-0.005-0.206-0.0810.1700.0060.051
Eosinophil count (×10⁹/L)0.0040.0040.174-0.0191.0000.0550.0530.1250.079-0.0350.0020.0370.115-0.091-0.043-0.0980.0510.052-0.0120.032
HIV viral load (copies/mL)0.054-0.141-0.144-0.5070.0551.000-0.093-0.2460.072-0.077-0.0610.052-0.1390.081-0.0280.1920.061-0.090-0.079-0.114
Hematocrit (%)-0.080-0.1210.0750.1740.053-0.0931.0000.1070.0280.050-0.2210.5680.088-0.0730.107-0.3060.3460.9790.1710.087
Lymphocyte count (×10⁹/L)-0.1110.1260.3900.1820.125-0.2460.1071.0000.3570.1900.1380.0670.624-0.0260.027-0.057-0.0690.1020.0150.097
Monocyte count (×10⁹/L)-0.0350.0630.236-0.0540.0790.0720.0280.3571.0000.4390.1520.0000.6030.1580.0330.118-0.0120.0250.0620.061
Neutrophil count (×10⁹/L)-0.1060.0550.1830.164-0.035-0.0770.0500.1900.4391.0000.2550.0000.8370.104-0.0340.121-0.0670.0510.0110.016
Platelet count (×10³/µL)-0.0550.0950.1510.0650.002-0.061-0.2210.1380.1520.2551.0000.1740.2580.025-0.0860.161-0.162-0.227-0.089-0.010
Sex0.1420.3630.0000.0720.0370.0520.5680.0670.0000.0000.1741.0000.0170.0000.0360.3010.5860.5670.1970.159
White blood cell count (×10³/µL)-0.1310.0880.3490.1790.115-0.1390.0880.6240.6030.8370.2580.0171.0000.092-0.0090.075-0.0660.0850.0290.055
body_temperature_celsius0.0130.0450.002-0.100-0.0910.081-0.073-0.0260.1580.1040.0250.0000.0921.000-0.0100.246-0.058-0.078-0.0180.023
diastolic_bp_mmHg0.3140.200-0.005-0.005-0.043-0.0280.1070.0270.033-0.034-0.0860.036-0.009-0.0101.0000.0170.1260.1140.7120.275
heart_rate_bpm0.0340.120-0.030-0.206-0.0980.192-0.306-0.0570.1180.1210.1610.3010.0750.2460.0171.000-0.161-0.302-0.1040.029
height_m0.103-0.360-0.015-0.0810.0510.0610.346-0.069-0.012-0.067-0.1620.586-0.066-0.0580.126-0.1611.0000.3410.2110.228
hemoglobin_g_dL-0.072-0.1110.0660.1700.052-0.0900.9790.1020.0250.051-0.2270.5670.085-0.0780.114-0.3020.3411.0000.1770.096
systolic_bp_mmHg0.2770.1320.0100.006-0.012-0.0790.1710.0150.0620.011-0.0890.1970.029-0.0180.712-0.1040.2110.1771.0000.261
weight_kg0.2310.8000.0030.0510.032-0.1140.0870.0970.0610.016-0.0100.1590.0550.0230.2750.0290.2280.0960.2611.000

Missing values

2025-11-24T23:49:37.642304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-24T23:49:37.791936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-24T23:49:37.902573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anonymous_patient_idPatient IDstudy_sourceprimary_dateAge (at enrolment)Sexlatitudelongitudeprovincecityjhb_subregionCD4 cell count (cells/µL)HIV viral load (copies/mL)HIV_statusHematocrit (%)White blood cell count (×10³/µL)Platelet count (×10³/µL)hemoglobin_g_dLLymphocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Monocyte count (×10⁹/L)Eosinophil count (×10⁹/L)Basophil count (×10⁹/L)BMI (kg/m²)weight_kgheight_mheart_rate_bpmsystolic_bp_mmHgdiastolic_bp_mmHgbody_temperature_celsiuscd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_applied
2162HEAT_39B060AAE05B01-0001JHB_Ezin_0022017-01-1730.0Male-26.204128.0473GautengJohannesburgCentral_JHB35.06641.0Positive46.05.89294.014.81.833.600.370.060.0223.91623966.71.6761.0133.082.036.70.01.0False
2163HEAT_777FE353096E01-0002JHB_Ezin_0022017-01-2334.0Male-26.204128.0473GautengJohannesburgCentral_JHB28.023851.0Positive43.02.90261.014.31.261.360.240.020.0227.64530877.11.6760.0130.084.036.10.01.0False
2164HEAT_870B72D986FC01-0003JHB_Ezin_0022017-01-1944.0Male-26.204128.0473GautengJohannesburgCentral_JHB25.758961.0Positive50.04.50216.016.62.071.790.450.100.0923.83624880.71.8468.0131.083.036.10.01.0False
2165HEAT_322176AC6C5D01-0004JHB_Ezin_0022017-01-1925.0Female-26.204128.0473GautengJohannesburgCentral_JHB25.85903.0Positive38.05.70281.012.12.332.820.440.060.0445.289256123.31.6574.0119.067.036.40.01.0False
2166HEAT_3CEA7B84617301-0005JHB_Ezin_0022017-01-2420.0Female-26.204128.0473GautengJohannesburgCentral_JHB12.4815081.0Positive37.05.55188.012.01.971.770.421.350.0328.66401874.31.6182.0114.071.036.10.01.0False
2167HEAT_58FF2F784CB201-0006JHB_Ezin_0022017-01-3033.0Female-26.204128.0473GautengJohannesburgCentral_JHB32.59680.0Positive40.05.67239.013.51.663.320.560.110.0330.85937579.01.6081.0138.084.036.20.01.0False
2168HEAT_8933F8561DFE01-0007JHB_Ezin_0022017-02-0123.0Female-26.204128.0473GautengJohannesburgCentral_JHB41.4512806.0Positive42.03.90238.014.01.192.460.200.040.0020.87005352.11.5886.096.067.037.00.01.0False
2169HEAT_B98EA6B0A97C01-0008JHB_Ezin_0022017-01-2430.0Female-26.204128.0473GautengJohannesburgCentral_JHB8.81179182.0Positive31.02.19266.010.10.711.120.270.070.0323.55555653.01.5081.0103.058.036.80.01.0False
2170HEAT_4F1746B9846F01-0009JHB_Ezin_0022017-01-2423.0Female-26.204128.0473GautengJohannesburgCentral_JHB25.4010611.0Positive33.03.01260.010.71.091.520.320.060.0231.95312581.81.6063.0106.078.036.10.01.0False
2171HEAT_28F38F9E703F01-0010JHB_Ezin_0022017-02-0120.0Female-26.204128.0473GautengJohannesburgCentral_JHB17.89443901.0Positive37.03.62154.011.62.680.650.290.000.0018.85038750.71.6481.0108.061.036.10.01.0False
anonymous_patient_idPatient IDstudy_sourceprimary_dateAge (at enrolment)Sexlatitudelongitudeprovincecityjhb_subregionCD4 cell count (cells/µL)HIV viral load (copies/mL)HIV_statusHematocrit (%)White blood cell count (×10³/µL)Platelet count (×10³/µL)hemoglobin_g_dLLymphocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Monocyte count (×10⁹/L)Eosinophil count (×10⁹/L)Basophil count (×10⁹/L)BMI (kg/m²)weight_kgheight_mheart_rate_bpmsystolic_bp_mmHgdiastolic_bp_mmHgbody_temperature_celsiuscd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_applied
3205HEAT_A874F236863103-0012JHB_Ezin_0022017-05-1718.0Female-26.204128.0473GautengJohannesburgCentral_JHB18.192461.0Positive41.04.95249.013.92.621.930.320.030.0531.09261274.71.5577.0102.071.036.70.01.0False
3206HEAT_03011F93A9A603-0015JHB_Ezin_0022017-03-2018.0Male-26.204128.0473GautengJohannesburgCentral_JHB2.6777566.0Positive41.03.04278.012.91.111.510.200.210.0115.51514345.91.7272.099.064.036.30.01.0False
3207HEAT_7114C2DCC58703-0016JHB_Ezin_0022017-08-0415.0Male-26.204128.0473GautengJohannesburgCentral_JHB2.86361889.0Positive38.06.29216.012.62.742.910.480.110.0517.99802843.81.5676.0112.072.036.10.01.0False
3208HEAT_834747041DAD03-0017JHB_Ezin_0022017-09-1318.0Male-26.204128.0473GautengJohannesburgCentral_JHB27.5251443.0Positive49.04.42312.016.41.262.610.520.020.0220.40816357.61.6878.0125.085.036.50.01.0False
3209HEAT_379EC7DAAA2003-0018JHB_Ezin_0022017-09-2018.0Female-26.204128.0473GautengJohannesburgCentral_JHB6.908022.0Positive39.05.81384.013.02.782.230.380.390.0329.65862578.81.6381.0112.082.036.00.01.0False
3210HEAT_5641CF725B8603-0019JHB_Ezin_0022017-10-0218.0Male-26.204128.0473GautengJohannesburgCentral_JHB14.611295.0Positive45.08.31193.014.72.395.240.630.020.0320.45196364.81.7890.0119.076.036.70.01.0False
3211HEAT_68507A3C970D03-0020JHB_Ezin_0022017-11-2217.0Male-26.204128.0473GautengJohannesburgCentral_JHB18.437856.0Positive45.04.47261.015.31.362.620.370.100.0218.80047258.91.7779.0135.083.036.30.01.0False
3212HEAT_A817A9AFFE7B03-0021JHB_Ezin_0022017-11-1618.0Female-26.204128.0473GautengJohannesburgCentral_JHB34.9126688.0Positive41.03.79272.013.32.101.420.150.100.0221.69314160.51.6758.0114.068.036.40.01.0False
3213HEAT_0463461C163A03-0022JHB_Ezin_0022018-02-1415.0Female-26.204128.0473GautengJohannesburgCentral_JHB0.63170902.0Positive37.03.38231.012.20.222.360.310.490.0022.24723051.41.5276.097.062.036.60.01.0False
3214HEAT_F27B40CADDBE03-0023JHB_Ezin_0022018-05-0418.0Female-26.204128.0473GautengJohannesburgCentral_JHB23.6861862.0Positive37.04.84344.011.72.162.190.410.070.0224.72898464.11.6175.0113.082.036.40.01.0False